Preface

In this era of AI technology surge, computing power is no longer just a technical term, but also a strategic resource, and io.net is a key player in this digital industrial revolution. It is more accurate to say that io.net is a decentralized computing power liberation movement rather than just a project.

What is io.net?

io.net is a decentralized high-performance computing network designed to meet the heavy computing power needs of machine learning, deep learning, and other applications. The core idea behind this network is to allow everyone to access GPU distributed computing resources at extremely low cost, which were previously only available to large cloud providers like AWS, GCP, and Azure. From a broader perspective, io.net is built on the concept of DePIN (Decentralized Physical Infrastructure Network), connecting idle GPU resources globally (such as data centers, idle mining equipment, Filecoin, Render, and other hardware networks) to create an open, flexible, scalable, and trustless computing platform.

Their vision is very straightforward: “Compute is the new oil.”— Computing power is the digital oil of this generation, and IO Coin is the fuel of this computing power economy.

Pain points of centralized cloud

Currently, mainstream AI/ML developers rely on centralized cloud platforms to access GPU resources, but the flaws of this system are becoming increasingly apparent:

1. Insufficient computing power
   The global demand for GPUs in the field of AI is exploding exponentially, but suppliers like AWS, GCP, and Azure are far from meeting the demand, often waiting for weeks to rent the required models.
2. Choose Less
   Even if you can rent a GPU, the options for location, model, latency, security, etc. are very limited, and you have to follow the rules of centralized platforms, with almost zero flexibility.
3. Expensive price
   Training models can cost tens of thousands of US dollars, and a large project may burn hundreds of thousands of dollars in computing power costs in just one month, which is simply impossible for small and medium-sized teams.

io.net liberates decentralized computing power

io.net builds its solution on a simple yet powerful core concept: aggregating underutilized GPUs worldwide to form a massive, elastic, and cost-effective decentralized computing network. This system allows engineers and developers to effortlessly access a large number of GPU resources, and tailor detailed configurations such as region and hardware according to their needs. io.net provides:

• Automatic Layout
• Fault tolerance
• Dynamic Scheduling
• Decentralized computing support (including distributed training, model serving, reinforcement learning, hyperparameter tuning, etc.)

It is not just a cloud replacement solution, but also a new computing infrastructure designed for the AI/ML era.

The operation mechanism of io.net

The overall design of io.net is optimized for Python computing workloads, especially suitable for AI/ML workflows, including the following application scenarios:

1. Model Inference and Service (Batch Inference & Model Serving)
   Once the model training is completed, the inference phase often requires rapid processing of large amounts of data, io.net enables developers to perform model inference and deployment in parallel on distributed GPUs, simplifying the entire model service process and improving efficiency.
2. Parallel Training
   The memory and computing power of a single device are ultimately limited, especially when facing large language models (LLMs) or multimodal models. Traditional methods have long been inadequate. io.net uses model and data parallelism strategies, combined with distributed computing frameworks, to break through single-machine bottlenecks.
3. Hyperparameter Tuning
   Hyperparameter experiments are naturally suitable for parallelization, and io.net provides comprehensive support, including automatic checkpoint storage, recording of best parameters, elastic resource configuration, etc., significantly reducing the time for model optimization.
4. Reinforcement Learning
   RL training typically involves extensive environment interactions and real-time feedback requirements, io.net integrates open-source RL packages, providing a simple API to support large-scale RL workloads, suitable for product-level distributed training.

$IO Token Economics

In the decentralized computing power network of io.net, $IO is not a supporting role, but the core of the entire system’s energy cycle. It is not just a simple payment token, but a powerful mechanism used to drive supply and demand matching, participate in governance, incentivize contributors, and even assist in capturing value. The following are some information related to the $IO token:

1. Fixed total amount

The total supply of $IO is hard capped at 800 million, including:

• 5 billion released at mainnet launch, used for community, contributors, early participants and liquidity.
• In a way that distributes 300 million tokens per hour, awards will gradually be given to suppliers and stakers of computing power over a period of 20 years.

This fixed supply + slow release model gives $IO a naturally healthier scarcity trait than centralized platforms, avoiding the risk of unlimited issuance diluting user assets.

2. Reward per hour

$IO adopts a scheduling logic similar to Bitcoin, but further refines it to distribute rewards every hour for long-term release. The specific rules are as follows:

• The inflation rate for the first year is 8%.
• Monthly decrease in the release rate by 1.02% (approximately 12% annually) until the total amount reaches the limit.

This makes the overall network have long-term stability, and also enables suppliers and stakers to have a stable and predictable income model, no longer relying on floating subsidies or marketing airdrops, It is a sustainable DePIN incentive design.

(As a function of the total emission pool, source: docs.io.net)

(Annual Inflation Rate, Source: docs.io.net)

(Based on the currency issued in the year and the remaining currency to be issued, source: docs.io.net)

3. Programmatic Destruction Mechanism

$IO does not simply distribute and forget; it also incorporates a dynamic burning mechanism, which is key to its deflationary design:

• The GPU cloud network of io.net (IOG Network) will generate revenue.
• The system will use these revenues to repurchase and burn $IO.
• The destruction ratio will automatically adjust according to the market price to dynamically balance supply and demand.

This means that the more people use io.net, the scarcer and more valuable $IO becomes. It is a self-driven value capture and anti-inflation design. Compared to the logic of traditional income = centralized company profits, the income here is used to strengthen the overall token ecosystem.

Start trading on io.net:https://www.gate.io/trade/IO_USDT

Summary

io.net is a revolutionary platform that truly brings the spirit of Web3 into the field of AI infrastructure. In a world where computing power is monopolized and AI is centralized, io.net attempts to use the power of decentralization to open another door to an open, free, and fair computing power economy. If blockchain liberated finance and decentralized community governance rewrote organizational models, then io.net will be a key link in liberating AI capabilities and computing power. We are at a crossroads of combining AI and DePIN, and the upcoming computing power battlefield is no longer just about who can provide the most, but about who can unleash global potential the fastest.